Add a config file for FER2013 (#1185)

## What this patch does to fix the issue.
Add a config file for learning FER2013 model.

I have not yet tuned hyper parameters. I'll make another PR about this.

## Link to any relevant issues or pull requests.
Closes #1174

blueoil/configs/core/classification/lmnet_v1_quantize_fer_2013.py

@@ -0,0 +1,92 @@
+# Copyright 2020 The Blueoil Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+from easydict import EasyDict
+import tensorflow as tf
+
+from blueoil.common import Tasks
+from blueoil.networks.classification.lmnet_v1 import LmnetV1Quantize
+from blueoil.datasets.fer_2013 import FER2013
+from blueoil.data_processor import Sequence
+from blueoil.pre_processor import (
+    Resize,
+    DivideBy255,
+)
+from blueoil.data_augmentor import (
+    Crop,
+    FlipLeftRight,
+    Pad,
+)
+from blueoil.quantizations import (
+    binary_mean_scaling_quantizer,
+    linear_mid_tread_half_quantizer,
+)
+
+IS_DEBUG = False
+
+NETWORK_CLASS = LmnetV1Quantize
+DATASET_CLASS = FER2013
+
+IMAGE_SIZE = [48, 48]
+BATCH_SIZE = 100
+DATA_FORMAT = "NHWC"
+TASK = Tasks.CLASSIFICATION
+CLASSES = DATASET_CLASS.classes
+
+MAX_STEPS = 100000
+SAVE_CHECKPOINT_STEPS = 1000
+KEEP_CHECKPOINT_MAX = 5
+TEST_STEPS = 1000
+SUMMARISE_STEPS = 100
+# pretrain
+IS_PRETRAIN = False
+PRETRAIN_VARS = []
+PRETRAIN_DIR = ""
+PRETRAIN_FILE = ""
+
+PRE_PROCESSOR = Sequence([
+    Resize(size=IMAGE_SIZE),
+    DivideBy255()
+])
+POST_PROCESSOR = None
+
+NETWORK = EasyDict()
+NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
+NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
+NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.cosine_decay
+# Train data num is 28709
+step_per_epoch = 28709 // BATCH_SIZE
+NETWORK.LEARNING_RATE_KWARGS = {'learning_rate': 0.1, 'decay_steps': 100000}
+NETWORK.IMAGE_SIZE = IMAGE_SIZE
+NETWORK.BATCH_SIZE = BATCH_SIZE
+NETWORK.DATA_FORMAT = DATA_FORMAT
+NETWORK.WEIGHT_DECAY_RATE = 0.0005
+NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer
+NETWORK.ACTIVATION_QUANTIZER_KWARGS = {
+    'bit': 2,
+    'max_value': 2
+}
+NETWORK.WEIGHT_QUANTIZER = binary_mean_scaling_quantizer
+NETWORK.WEIGHT_QUANTIZER_KWARGS = {}
+
+# dataset
+DATASET = EasyDict()
+DATASET.BATCH_SIZE = BATCH_SIZE
+DATASET.DATA_FORMAT = DATA_FORMAT
+DATASET.PRE_PROCESSOR = PRE_PROCESSOR
+DATASET.AUGMENTOR = Sequence([
+    Pad(2),
+    Crop(size=IMAGE_SIZE),
+    FlipLeftRight(),
+])